code: def __init__(
      self,
      depth,
      stage=3,
      stride=2,
      dilation=1,
      style="pytorch",
      norm_cfg=dict(type="BN", requires_grad=True),
      norm_eval=True,
      with_cp=False,
      dcn=None,
      pretrained=None,
      init_cfg=None,
  ):
      super(ResLayer, self).__init__(init_cfg)
  
      self.norm_eval = norm_eval
      self.norm_cfg = norm_cfg
      self.stage = stage
      self.fp16_enabled = False
      block, stage_blocks = ResNet.arch_settings[depth]
      stage_block = stage_blocks[stage]
      planes = 64 * 2**stage
      inplanes = 64 * 2 ** (stage - 1) * block.expansion
  
      res_layer = _ResLayer(
          block,
          inplanes,
          planes,
          stage_block,
          stride=stride,
          dilation=dilation,
          style=style,
          with_cp=with_cp,
          norm_cfg=self.norm_cfg,
          dcn=dcn,
      )
      self.add_module(f"layer{stage + 1}", res_layer)
  
      assert not (
          init_cfg and pretrained
      ), "init_cfg and pretrained cannot be specified at the same time"
      if isinstance(pretrained, str):
          warnings.warn(
              "DeprecationWarning: pretrained is a deprecated, "
              'please use "init_cfg" instead'
          )
          self.init_cfg = dict(type="Pretrained", checkpoint=pretrained)
      elif pretrained is None:
          if init_cfg is None:
              self.init_cfg = [
                  dict(type="Kaiming", layer="Conv2d"),
                  dict(
                      type="Constant",
                      val=1,
                      layer=["_BatchNorm", "GroupNorm"],
                  ),
              ]
      else:
          raise TypeError("pretrained must be a str or None")
  
file_path: mmdetection/mmdet/models/roi_heads/shared_heads/res_layer.py
imports: import warnings
  import torch.nn
  from mmengine.model import BaseModule
  from mmdet.models.backbones import ResNet
  from mmdet.models.layers import ResLayer
  from mmdet.registry import MODELS
label_desc: Use image node_id 1 to create a new ResLayer object from inherited base classes: BaseModule with example: obj = ResLayer(depth, stage, stride, dilation, style, norm_cfg, norm_eval, with_cp, dcn, pretrained, init_cfg)